[amelia] Data Consideration for Amelia

Dr. King, I'm sorry to keep pestering you with these questions; unfortunately, there is no one in my department with the kind of mathmatical training necessary to completely decipher the EMis algorithm ;-). I think I understand what you proscribe in King et al (2001) but I'd like to confirm it. (please note, I have not tried any of the following - I'm using an old PII with 128mb of RAM. Every attempt to run Amelia is a major chore - having to shut down all other background routines, etc. So, this time I'm asking first ...) Presume I have variable that counts the number of mentions (in an open-ended context) of a particular subject (in this case, crime as the "biggest problem"). This seems to me to be most closely related to a event-count type variable, implying that I should take the square root (or some partial power) to stablize the variance, etc. However, conceptually this variable is measuring salience which must be bounded at zero, implying I should be applying some form of non-linear (logistic, perhaps) transformation. Empirically, because of the significant positive skew of the variable, Amelia returns a large number of negative imputed values. What can be done about this situation (aside from springing for Gauss - which my department won't do)? I can see three options: 1. impute from the raw data, either leaving the continuous values or truncating any negative imputations to zero. 2. impute from transformed data using one or the other transformation 3. impute from the transformed data using both transformations Which seems best to you? If I do apply a transformation before imputation, do I "un-transform" the data after (in the case of the square-root, I'd say no because any negative imputations produced by Amelia would have their signs reversed...) Again, thank you for your time -- Matthew "ElectricBlooz" Vile UNO Survey Research Center http://www.swd.org/mardi/blooz.htm - amelia mailing list served by Harvard-MIT Data Center List Address: amelia(a)latte.harvard.edu Subscribe/Unsubscribe: http://lists.hmdc.harvard.edu/?info=amelia